Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors)

Demonstrates how to use sensitivity analysis to explain time-series forecasts using the ahead::dynrmf model with external regressors in R. The approach evaluates how changes in external regressors impact forecast outputs. Two examples are shown: US Consumption vs Income and TV Advertising vs Insurance Quotes, each using both Ridge regression and SVM (via e1071) as the underlying fit function. The ahead::dynrmf_sensi and ahead::plot_dynrmf_sensitivity functions handle computation and visualization of sensitivities.